“The NESD program looks ahead to a future in which advanced neural devices offer improved fidelity, resolution, and precision sensory interface for therapeutic applications,” said Phillip Alvelda, the founding NESD Program Manager. “By increasing the capacity of advanced neural interfaces to engage more than one million neurons in parallel…”

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A Brown University team led by Dr. Arto Nurmikko will seek to decode neural processing of speech, focusing on the tone and vocalization aspects of auditory perception. The team’s proposed interface would be composed of networks of up to 100,000 untethered, submillimeter-sized “neurograin” sensors implanted onto or into the cerebral cortex. A separate RF unit worn or implanted as a flexible electronic patch would passively power the neurograins and serve as the hub for relaying data to and from an external command center that transcodes and processes neural and digital signals.

A Columbia University team led by Dr. Ken Shepard will study vision and aims to develop a non-penetrating bioelectric interface to the visual cortex. The team envisions layering over the cortex a single, flexible complementary metal-oxide semiconductor (CMOS) integrated circuit containing an integrated electrode array. A relay station transceiver worn on the head would wirelessly power and communicate with the implanted device.

A Fondation Voir et Entendre team led by Drs. Jose-Alain Sahel and Serge Picaud will study vision. The team aims to apply techniques from the field of optogenetics to enable communication between neurons in the visual cortex and a camera-based, high-definition artificial retina worn over the eyes, facilitated by a system of implanted electronics and micro-LED optical technology.

A John B. Pierce Laboratory team led by Dr. Vincent Pieribone will study vision. The team will pursue an interface system in which modified neurons capable of bioluminescence and responsive to optogenetic stimulation communicate with an all-optical prosthesis for the visual cortex.

A Paradromics, Inc., team led by Dr. Matthew Angle aims to create a high-data-rate cortical interface using large arrays of penetrating microwire electrodes for high-resolution recording and stimulation of neurons. As part of the NESD program, the team will seek to build an implantable device to support speech restoration. Paradromics’ microwire array technology exploits the reliability of traditional wire electrodes, but by bonding these wires to specialized CMOS electronics the team seeks to overcome the scalability and bandwidth limitations of previous approaches using wire electrodes.

A University of California, Berkeley, team led by Dr. Ehud Isacoff aims to develop a novel “light field” holographic microscope that can detect and modulate the activity of up to a million neurons in the cerebral cortex. The team will attempt to create quantitative encoding models to predict the responses of neurons to external visual and tactile stimuli, and then apply those predictions to structure photo-stimulation patterns that elicit sensory percepts in the visual or somatosensory cortices, where the device could replace lost vision or serve as a brain-machine interface for control of an artificial limb.

Having found a powerful method for activating neurons with blue light in the protein Channelrhodopsin-2 (ChR2) [1], we sought to augment the toolbox by finding a single-component system capable of mediating light-elicited neuronal inhibition. We identified a powerful tool, the mammalian codon-optimized version of the light-driven chloride pump halorhodopsin, from the archaebacterium Natronobacterium pharaonis (here abbreviated Halo) [2].

What is the right level of biological realism to model neuronal systems in order to understand their computational properties? Some recent papers may help shed some light on the subject. Models of the computational properties of local networks of neurons are starting to come into their own. This year has already seen at least two articles published in experimentalist journals based on the same core of theoretical work.

To bring you up to speed, I need to remind you what is going on in the world of experimental neuroscience.

By using multi-electrode arrays or optical imaging, investigators can now record from many individual neurons in various parts of nervous systems simultaneously while an animal performs sensory, motor or cognitive tasks. Given the large multidimensional datasets that are now routinely generated, it is often not obvious how to find meaningful results within the data.

This paper goes on to provide a nice overview on mathematical methods that researchers are using to grapple with the challenge of understanding the dynamics of the neural systems they are recording from. They make the case that conceptual progress needs to be made on the interpretation of the data these results yield. How can we understand what computations these neurons are collectively performing?

Our brains have a lot of problems that need to be solved — now. And neurotechnology is a hot field. But what knowledge and skills do you study if you want to be a neurotechnologist? What problems are important, but also tractable within a reasonable timeframe? And, can you survive while climbing this possibly-very-high mountain?

A team of three academics at MIT and the University of Hong Kong is launching an international collaboration to create a set of novel courses to address this need. The first one, Neurotechnology Ventures, is being taught in Spring 2007 and focuses on neurotechnologies that are close to solving major human problems. The class explores the problems that neurotechnologists encounter when envisioning, planning, and building startups to bring neuroengineering innovations to the world.

Emphasizing the global nature of any modern neurotechnology, Neurotechnology Ventures will be videoconferenced between the U.S. and China, which is increasingly becoming a major neurotechnology player (including some very daring and scientifically interesting developments in fields such as human spinal cord regenerative medicine). Information will be posted online as the class evolves dynamically, to the web site HTTP://Neuroven.Media.MIT.edu. The goal is to open up this new field to the world, and see if we can solve the major problems of the brain in an open and efficient way.

Dear Members,
I am a prospective graduate student interested in taking up Neural Engineering under EE or Biomedical Engg for research. But I have a lot of concerns and need help from a person who knows about the field well.
1. I have studied VLSI, DSP, Image Processing, Wireless Communication, Control Systems and Embedded Systems as graduate and undergraduate courses and have some research interest in Neural Networks and Machine Learning(That’s how I got interested in Neural Engg and Prosthetics). Which of these subjects will be of help in Neural Engg/Prosthetics research. Which will be of most relevance. Please list them in the order of relevance(high->low).
2. What are the applications of the research ?
3. What is the research and JOB scope for this field? Are there any companies who recruit people with this specialisation? How is the job scene in academia? How many univs are doing research in this field in US? Please let me know about the career progression in academia, like how much time does it take to get full time academic position after PhD?
4. Especially, what are the applications of this research in Robotics?
5. What are the current problems and research themes in universities?
6. What imaging technologies are used in this research?

Though my queries may seem a bit ameteuristic, it is very important for me to get clarity on these doubts.
Hope my queries will be answered.
Thanking all of you in advance,
sudhi

Today MIT’s Technology Review magazine released its annual list of innovators under the age of 35 who were nominated for recognition. Interestingly, almost a full quarter are doing work relating to or impacting the field of neuroengineering — including ways to tag synapses with quantum dots, activate neurons remotely, improve machine vision, classify whole-brain states for prosthetic purposes, and make nanowire arrays.